I agree with Nick's comments.
More generally, when the data does not fit your model, it is usually better to look for a different model than to try to make your data fit. Transformations should serve your purposes, not the model's purposes. In your particular case, it is hard to imagine any transformation that would make it even remotely normal without totally destroying the data.
Note that LDA doesn't assume the data are normal overall, it assumes the data are normal at each level of the grouping variable. That may be the case in your data.
If not, then there are lots of alternatives; two that spring immediately to mind are logistic regression and classification trees.