I think the best answer is "B. Start over with a new sample of 120 students from this year's freshman class.", given that you can still use the 90 answers you already have (I don't see why not). This would give the largest sample size.
If questionnaire's goal is to discover "Why don't students respond", then you could repeatedly random sample 120 students until, by chance, they all respond
You'd then have a lot of student data, but it is completely skewed
This seems like it introduces another flavor of selection bias. You're still selecting a sample composed of people who respond and ignoring those who don't. I don't have a better answer, though.
So "Are students academic enough to understand this questionnaire, or wheelchair bound to stop them posting responses?"
The skew still stands
A meta point is it is probably impossible to remove bias
EDIT: sorry, didn't see you're the person I replied to. I think you made a very good choice -- in the face of the unknown, increasing sample size and attempting to make it unbiased is possibly the best step
