Doing predictive modeling on predicted value
Your approach seems valid. It is not even biaised because you don't use other features than A to build F.

Performing unsupervised learning prior to supervised learning in a very common approach, and there are numerous types of workflows where supervised and unsupervised techniques are combined to solve the problem.

However, be careful with random forest feature importance: there are several ways to compute importances, and not all will give the same results. There are also other techniques to make complex models interpretable, such as LIME for local explanation or SHAP values for both local and global explanation.