Success of a book depends on various intrinsic and extrinsic parameters. We perform a cross-platform study of book reading behavior on Goodreads and attempt to establish the connection between the Goodreads entities and the Amazon best sellers. We analyze the collective reading behavior on Goodreads platform and quantify various characteristic features of the Goodreads entities to identify differences between the Amazon best sellers (ABS) and the other non-best selling books. Using these features we devise a model to predict if a book shall become a best seller after one month (15 days) since its publication. On a balanced set, we are able to achieve a very high avg. accuracy of 88.72% (85.66%) for the prediction where the other competitive class contains books which are randomly selected from the Goodreads dataset. Our method primarily based on features derived from user posts and genre related characteristic properties achieves an improvement of 16.4% over the traditional popularity factors (ratings, reviews) based baseline methods.