Traditional computer-aided design (CAD) is highly specialized and time-consuming, posing a significant barrier for users without 3D modeling expertise. Even with the recent surge of AI-based methods, CAD modeling remains challenging due to three fundamental issues: 1) the lack of simple yet controllable interaction modalities; 2) the absence of 3D representations that are both amenable to learning paradigms and faithful to CAD characteristics; and 3) the scarcity of large-scale datasets consisting of inconsistent modeling sequences. To address these challenges, we propose SelfSketch2CAD, a framework that leverages geometric priors for self-supervised CAD modeling sequence learning from single-view freehand sketches. Our method leverages predefined differentiable CAD modeling operators, combined with an implicit signed distance function (SDF) representation, to automatically infer CAD modeling sequences from 2D sketches, and composes primitive-level SDFs via the union operation into a holistic SDF field, enabling supervision solely from target shapes without requiring ground-truth modeling sequences. To mitigate the inherent ambiguity of sparse line drawings, we further introduce a multifaceted sketch perception module, which uses a U-Net to predict depth and normal maps from the input sketch, applies a pre-trained DINO encoder to extract features from both the sketch and its inferred geometric cues, and finally fuses these features through a multi-stage cross-attention mechanism to guide subsequent CAD operator prediction. Extensive experiments on the ABC dataset show that our approach consistently outperforms existing methods in both quantitative metrics and qualitative visual fidelity, demonstrating its effectiveness for sketch-based CAD content creation. The source code will be released for research use.