I think it would be better to see some fields of Computer Science yourself so I shall provide you with links you might rather enjoy clicking for a few seconds each.
https://www.cs.cmu.edu/~ILIM/publications/PDFs/W-THESIS09.pdf
http://www.cs.columbia.edu/~jebara/6772/notes/notes11b.pdf
http://www.cs.columbia.edu/~rocco/Teaching/S14/Readings/bchks-journ.pdf
http://www.csc.kth.se/utbildning/kth/kurser/DD2442/semteo14/lecturenotes/NotesLec16.pdf
http://people.csail.mit.edu/cpeikert/pubs/lossy_tdf.pdf
I don’t know. Make the judgements yourself. But depending on the branch you get into for CS in college, you could be exposed to ideas that utilize pretty much more or less all the undergraduate and graduate courses in mathematics. So ya, if you follow the more theoretical computer science, more than likely, CS will become more and more like mathematics (as in lots of rigorous proofs, etc.).
That said, most people who graduate in CS end up going to the workforce in which addition and subtraction is more than plenty for a career (and pretty much all of them take CS courses that really don’t even require the basics of Integrals so the links I have above are not the norm by any measures for CS majors -as they are usually considered graduate level courses-).
I (nor anyone here for that matter) cannot guarantee you would be ‘good in CS’ if you did well in math. But don’t fret and at least give a go if you are interested in college. Anyways, if you are wondering how Calc 2 plays direct role, a simple look would be at the Python Sklearn.
http://scikit-learn.org/stable/index.html
Much of basic machine learning packages used in the workforce today that have been stringed up through sheer Integrals and Derivatives. Quite fascinating is it not? 