Writer identification/recognition from off-line Arabic handwriting on sentence-level is still a tough task. In this paper, we start by investigating the performance of textural extractors for writer identification of divergent writing types. Taking into account their strengths and limits, we propose a new method that keeps the main features of the writing and handles the sensitivity of systems towards the available samples of text at the pre-processing phase. We also analyze the influence of the handwriting types on the efficiency of the writer identification process. In this regard, we perform a comparative study between handcrafted and automated features. Under multiple classifiers (RF, XGB, KNN and SVM). We find that writers with good and well clear handwriting have fewer similarities, thus, provides enhanced experimental identification rates. However, Bad handwriting presents more similarities between the writers, which explains the reduction in the identification rate.