Is it appropriate to use random forest not for prediction but to only gain insights on variable importance?
A Random Forest model can definitely be used to help you determine feature importances. Actually, it is used as a very common strategy for feature selection. If your data is too small, my recommendation would be to treat it as if you were to make predictions with this model, meaning that you should watch out for overfitting and do a proper hyperparameter optimization (you want a simpler and smaller model here), so that the features you find are really the most relevant, and don't just appear important for being biased by the small training set.